In [1]:
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

Read in Data

In [2]:
matrix_data = pd.read_csv("PROKKA.matrix.data", names=['Gene Count', 'Phage 1', 'Phage 2'], sep=",")
In [3]:
unique_genome = matrix_data['Phage 1'].unique().tolist()
In [4]:
len(matrix_data)
Out[4]:
36481

Generate A Pairwise Count Matrix

To do so, our strategy is the following:

  1. Get all the unique phages
  2. We get all the unique values of phages, and the corresponding array of counts
  3. Get a pandas series
  4. Create a numpy matrix from the obtained series (should be size 190x190)
In [5]:
phage_list = matrix_data['Phage 1'].unique()
print(f"Number of unique phages: {len(phage_list)}")

Number of unique phages: 191

Now moving on to getting the pairwise counts:

In [6]:
# you want to get all the unique Phage 1 values, and the corresponding gene counts for every other phage, including itself
# here with group by we get, and now we want to get all the values as a list
c_matrix = matrix_data.groupby('Phage 1')['Gene Count'].apply(list)

above will result in a pandas series. Now to finally get what we need, we need to turn into an array, but first let's check the type:

In [7]:
type(c_matrix)
Out[7]:
pandas.core.series.Series
In [8]:
# c_matrix.tolist()
numpy_matrix = np.asarray(c_matrix.tolist())

Now let's check out the matrix we got:

In [9]:
print(numpy_matrix)
print(f"\n Matrix has the shape: {numpy_matrix.shape}")

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]

 Matrix has the shape: (191, 191)
In [10]:
np.savetxt("prokka_matrix.csv", numpy_matrix, delimiter=",")

which is exactly what we wanted!

Make a Dataframe with the Count Matrix

In [11]:
pairwise_count = pd.DataFrame(data = numpy_matrix)
In [12]:
pairwise_count.columns = list(phage_list)
In [13]:
pairwise_count.index = list(phage_list)
In [14]:
pairwise_count
Out[14]:
GCA_000009005.1_ASM900v1_genomic.gbff_pp10.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp11.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp12.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp13.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp14.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp15.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp16.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp17.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp18.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp19.ffn ... GCA_900081425.1_12641_2_75_genomic.gbff_pp21.ffn GCA_900081425.1_12641_2_75_genomic.gbff_pp22.ffn GCA_900081945.1_12673_2_15_genomic.gbff_pp10.ffn GCA_900124925.1_17138_2_42_genomic.gbff_pp13.ffn GCA_900126435.1_17175_2_81_genomic.gbff_pp6.ffn GCA_900251565.1_M3684_genomic.gbff_pp14.ffn GCA_900251895.1_M3925_genomic.gbff_pp17.ffn GCA_900251895.1_M3925_genomic.gbff_pp18.ffn GCA_900323905.1_24117-WT_genomic.gbff_pp6.ffn GCA_900482705.1_7915_6_43_genomic.gbff_pp16.ffn
GCA_000009005.1_ASM900v1_genomic.gbff_pp10.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_000009005.1_ASM900v1_genomic.gbff_pp11.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_000009005.1_ASM900v1_genomic.gbff_pp12.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_000009005.1_ASM900v1_genomic.gbff_pp13.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_000009005.1_ASM900v1_genomic.gbff_pp14.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
GCA_900251565.1_M3684_genomic.gbff_pp14.ffn 0 0 0 0 0 0 7 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_900251895.1_M3925_genomic.gbff_pp17.ffn 0 0 0 0 0 0 0 8 0 0 ... 0 0 0 0 0 0 0 0 0 4
GCA_900251895.1_M3925_genomic.gbff_pp18.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_900323905.1_24117-WT_genomic.gbff_pp6.ffn 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
GCA_900482705.1_7915_6_43_genomic.gbff_pp16.ffn 0 0 0 0 0 0 0 4 0 0 ... 0 0 0 0 0 0 4 0 0 0

191 rows × 191 columns

Example of plotting heatmaps for this dataframe we generated

I think the best way to visualize this data now is to do a heatmap, and pandas provides a very nice way of doing this with the actual numbers in them. Here is how:

In [15]:
ax = sns.heatmap(uniform_data, linewidth=0.5)
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-15-4fbd8887ca0c> in <module>
----> 1 ax = sns.heatmap(uniform_data, linewidth=0.5)
      2 plt.show()

NameError: name 'uniform_data' is not defined
In [16]:
pairwise_count.style.background_gradient(cmap='Blues')
Out[16]:
GCA_000009005.1_ASM900v1_genomic.gbff_pp10.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp11.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp12.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp13.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp14.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp15.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp16.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp17.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp18.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp19.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp1.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp20.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp21.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp22.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp2.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp3.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp4.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp5.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp6.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp7.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp8.ffn GCA_000009005.1_ASM900v1_genomic.gbff_pp9.ffn GCA_000009585.1_ASM958v1_genomic.gbff_pp19.ffn GCA_000009585.1_ASM958v1_genomic.gbff_pp5.ffn GCA_000009585.1_ASM958v1_genomic.gbff_pp9.ffn GCA_000009645.1_ASM964v1_genomic.gbff_pp1.ffn GCA_000009645.1_ASM964v1_genomic.gbff_pp2.ffn GCA_000009665.1_ASM966v1_genomic.gbff_pp4.ffn GCA_000010445.1_ASM1044v1_genomic.gbff_pp2.ffn GCA_000010445.1_ASM1044v1_genomic.gbff_pp4.ffn GCA_000010465.1_ASM1046v1_genomic.gbff_pp2.ffn GCA_000010465.1_ASM1046v1_genomic.gbff_pp4.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp11.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp18.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp1.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp22.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp2.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp5.ffn GCA_000011505.1_ASM1150v1_genomic.gbff_pp6.ffn GCA_000012045.1_ASM1204v1_genomic.gbff_pp18.ffn GCA_000012045.1_ASM1204v1_genomic.gbff_pp8.ffn GCA_000013425.1_ASM1342v1_genomic.gbff_pp6.ffn GCA_000013465.1_ASM1346v1_genomic.gbff_pp16.ffn GCA_000013465.1_ASM1346v1_genomic.gbff_pp21.ffn GCA_000016805.1_ASM1680v1_genomic.gbff_pp8.ffn GCA_000016805.1_ASM1680v1_genomic.gbff_pp9.ffn GCA_000017085.1_ASM1708v1_genomic.gbff_pp19.ffn GCA_000024585.1_ASM2458v1_genomic.gbff_pp3.ffn GCA_000025145.2_ASM2514v1_genomic.gbff_pp18.ffn GCA_000025145.2_ASM2514v1_genomic.gbff_pp20.ffn GCA_000025145.2_ASM2514v1_genomic.gbff_pp8.ffn GCA_000027045.1_ASM2704v1_genomic.gbff_pp18.ffn GCA_000144955.1_ASM14495v1_genomic.gbff_pp20.ffn GCA_000159535.2_ASM15953v2_genomic.gbff_pp2.ffn GCA_000160355.1_ASM16035v1_genomic.gbff_pp17.ffn GCA_000160355.1_ASM16035v1_genomic.gbff_pp21.ffn GCA_000160355.1_ASM16035v1_genomic.gbff_pp3.ffn GCA_000160375.1_ASM16037v1_genomic.gbff_pp21.ffn GCA_000160395.1_ASM16039v1_genomic.gbff_pp19.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp11.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp12.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp13.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp14.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp2.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp4.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp6.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp7.ffn GCA_000160415.1_ASM16041v1_genomic.gbff_pp9.ffn GCA_000162595.1_ASM16259v1_genomic.gbff_pp5.ffn GCA_000162655.1_ASM16265v1_genomic.gbff_pp2.ffn GCA_000162815.1_ASM16281v1_genomic.gbff_pp21.ffn GCA_000162815.1_ASM16281v1_genomic.gbff_pp5.ffn GCA_000162855.1_ASM16285v1_genomic.gbff_pp18.ffn GCA_000164715.1_ASM16471v1_genomic.gbff_pp2.ffn GCA_000172855.1_ASM17285v1_genomic.gbff_pp19.ffn GCA_000174495.1_ASM17449v1_genomic.gbff_pp21.ffn GCA_000174515.1_ASM17451v1_genomic.gbff_pp10.ffn GCA_000174515.1_ASM17451v1_genomic.gbff_pp22.ffn GCA_000174575.1_ASM17457v1_genomic.gbff_pp8.ffn GCA_000174615.1_ASM17461v1_genomic.gbff_pp16.ffn GCA_000187145.1_ASM18714v1_genomic.gbff_pp21.ffn GCA_000187165.1_ASM18716v1_genomic.gbff_pp16.ffn GCA_000215405.2_ASM21540v2_genomic.gbff_pp1.ffn GCA_000242515.2_ASM24251v2_genomic.gbff_pp16.ffn GCA_000242555.2_ASM24255v2_genomic.gbff_pp2.ffn GCA_000248515.2_ASM24851v2_genomic.gbff_pp9.ffn GCA_000248615.2_ASM24861v2_genomic.gbff_pp18.ffn GCA_000248735.2_ASM24873v2_genomic.gbff_pp16.ffn GCA_000248795.2_ASM24879v2_genomic.gbff_pp12.ffn GCA_000248795.2_ASM24879v2_genomic.gbff_pp17.ffn GCA_000248995.2_ASM24899v2_genomic.gbff_pp9.ffn GCA_000262835.1_VRSA02_3_genomic.gbff_pp10.ffn GCA_000276625.1_Newbould305_assembly_v1_genomic.gbff_pp16.ffn GCA_000359825.1_Stap_aure_HI049B_V1_genomic.gbff_pp16.ffn GCA_000360825.1_Stap_aure_M0334_V1_genomic.gbff_pp10.ffn GCA_000360905.1_Stap_aure_M0351_V1_genomic.gbff_pp11.ffn GCA_000361645.1_Stap_aure_M0602_V1_genomic.gbff_pp10.ffn GCA_000362125.1_Stap_aure_M0877_V1_genomic.gbff_pp18.ffn GCA_000362285.1_Stap_aure_M0946_V1_genomic.gbff_pp10.ffn GCA_000363845.1_Stap_aure_B40950_V1_genomic.gbff_pp10.ffn GCA_000439835.1_LCT-SA67_genomic.gbff_pp21.ffn GCA_000443265.1_S123v1_genomic.gbff_pp1.ffn GCA_000525445.1_Stap_aure_DAR5883_V1_genomic.gbff_pp16.ffn GCA_000531555.1_Stap_aure_M0032_V1_genomic.gbff_pp18.ffn GCA_000532975.1_Stap_aure_M1299_V1_genomic.gbff_pp15.ffn GCA_000534835.1_Saureus301-188_genomic.gbff_pp14.ffn GCA_000534835.1_Saureus301-188_genomic.gbff_pp9.ffn GCA_000535875.1_Stap_aure_M1379_V1_genomic.gbff_pp1.ffn GCA_000536015.1_Stap_aure_M1473_V1_genomic.gbff_pp9.ffn GCA_000536935.1_Stap_aure_LAMC6094_V1_genomic.gbff_pp13.ffn GCA_000537595.1_Stap_aure_UCIM6019_V1_genomic.gbff_pp10.ffn GCA_000537595.1_Stap_aure_UCIM6019_V1_genomic.gbff_pp11.ffn GCA_000540235.1_Stap_aure_UCIM6050_V1_genomic.gbff_pp20.ffn GCA_000540235.1_Stap_aure_UCIM6050_V1_genomic.gbff_pp9.ffn GCA_000540615.1_Stap_aure_MISS6027_V1_genomic.gbff_pp1.ffn GCA_000543485.1_Stap_aure_M0773_V1_genomic.gbff_pp9.ffn GCA_000544885.1_Stap_aure_M0887_V1_genomic.gbff_pp6.ffn GCA_000545865.1_Stap_aure_M0966_V1_genomic.gbff_pp10.ffn GCA_000546485.1_Stap_aure_M1022_V1_genomic.gbff_pp6.ffn GCA_000547205.1_Stap_aure_M1090_V1_genomic.gbff_pp19.ffn GCA_000547345.1_Stap_aure_M1562_V1_genomic.gbff_pp1.ffn GCA_000547705.1_Stap_aure_M0758_V1_genomic.gbff_pp11.ffn GCA_000550985.1_Stap_aure_M0012_V1_genomic.gbff_pp10.ffn GCA_000551445.1_Stap_aure_M0046_V1_genomic.gbff_pp8.ffn GCA_000553735.1_Stap_aure_M0219_V1_genomic.gbff_pp5.ffn GCA_000553815.1_Stap_aure_M0226_V1_genomic.gbff_pp10.ffn GCA_000555085.1_Stap_aure_M0355_V1_genomic.gbff_pp17.ffn GCA_000555845.1_Stap_aure_M0421_V1_genomic.gbff_pp7.ffn GCA_000556025.1_Stap_aure_M0437_V1_genomic.gbff_pp17.ffn GCA_000556445.1_Stap_aure_M0471_V1_genomic.gbff_pp17.ffn GCA_000556705.1_Stap_aure_M0496_V1_genomic.gbff_pp17.ffn GCA_000559825.1_Stap_aure_M41648_V1_genomic.gbff_pp1.ffn GCA_000560925.1_Stap_aure_W42932_V1_genomic.gbff_pp21.ffn GCA_000562005.1_Stap_aure_T84595_V1_genomic.gbff_pp10.ffn GCA_000562305.1_Stap_aure_F89468_V1_genomic.gbff_pp10.ffn GCA_000562525.1_Stap_aure_H71785_V1_genomic.gbff_pp16.ffn GCA_000570015.1_Stap_aure_H87628_V1_genomic.gbff_pp17.ffn GCA_000570015.1_Stap_aure_H87628_V1_genomic.gbff_pp19.ffn GCA_000575965.1_Stap_aure_M1112_V1_genomic.gbff_pp18.ffn GCA_000578295.1_Stap_aure_T77565_V1_genomic.gbff_pp18.ffn GCA_000586815.1_Stap_aure_W28921_V1_genomic.gbff_pp14.ffn GCA_000586855.1_Stap_aure_W19270_V1_genomic.gbff_pp7.ffn GCA_000587515.1_Stap_aure_H65881_V1_genomic.gbff_pp10.ffn GCA_000594305.1_Stap_aure_M49691_V1_genomic.gbff_pp10.ffn GCA_000596365.1_Stap_aure_DAR5864_V1_genomic.gbff_pp14.ffn GCA_000601945.1_Stap_aure_F87602_V1_genomic.gbff_pp6.ffn GCA_000602085.1_Stap_aure_T16619_V1_genomic.gbff_pp18.ffn GCA_000604485.1_Stap_aure_W53395_V1_genomic.gbff_pp16.ffn GCA_000605905.1_Stap_aure_W25814_V1_genomic.gbff_pp19.ffn GCA_00063575.1_ASM16357v1_genomic.gbff_pp4.ffn GCA_000641035.1_Stap_aure_VET0086R_V1_genomic.gbff_pp21.ffn GCA_001046295.1_ASM104629v1_genomic.gbff_pp17.ffn GCA_001060985.1_ASM106098v1_genomic.gbff_pp13.ffn GCA_001194545.1_7907_4_56_genomic.gbff_pp13.ffn GCA_001226685.1_6401_6_9_genomic.gbff_pp6.ffn GCA_001234105.1_5295_1_7_genomic.gbff_pp18.ffn GCA_001289825.1_11641_1_62_genomic.gbff_pp11.ffn GCA_001352295.1_7748_4_33_genomic.gbff_pp22.ffn GCA_001411685.1_ASM141168v1_genomic.gbff_pp2.ffn GCA_001548295.1_ASM154829v1_genomic.gbff_pp10.ffn GCA_001996485.1_ASM199648v1_genomic.gbff_pp2.ffn GCA_002121705.1_ASM212170v1_genomic.gbff_pp4.ffn GCA_002121705.1_ASM212170v1_genomic.gbff_pp5.ffn GCA_002121705.1_ASM212170v1_genomic.gbff_pp6.ffn GCA_002122405.1_ASM212240v1_genomic.gbff_pp10.ffn GCA_002122685.1_ASM212268v1_genomic.gbff_pp21.ffn GCA_002127025.1_ASM212702v1_genomic.gbff_pp10.ffn GCA_002225345.1_ASM222534v1_genomic.gbff_pp11.ffn GCA_002274895.1_UCSF14054_v1.0_genomic.gbff_pp2.ffn GCA_002275265.1_ISU-927_v1.0_genomic.gbff_pp16.ffn GCA_002904975.1_ASM290497v1_genomic.gbff_pp10.ffn GCA_002983095.1_ASM298309v1_genomic.gbff_pp13.ffn GCA_002993865.1_ASM299386v1_genomic.gbff_pp9.ffn GCA_003237795.1_ASM323779v1_genomic.gbff_pp10.ffn GCA_003240215.1_ASM324021v1_genomic.gbff_pp21.ffn GCA_003420165.1_ASM342016v1_genomic.gbff_pp15.ffn GCA_003422165.1_ASM342216v1_genomic.gbff_pp20.ffn GCA_900035925.1_7712_8_30_genomic.gbff_pp17.ffn GCA_900040545.1_7469_7_44_genomic.gbff_pp11.ffn GCA_900080895.1_12625_5_54_genomic.gbff_pp2.ffn GCA_900081425.1_12641_2_75_genomic.gbff_pp1.ffn GCA_900081425.1_12641_2_75_genomic.gbff_pp21.ffn GCA_900081425.1_12641_2_75_genomic.gbff_pp22.ffn GCA_900081945.1_12673_2_15_genomic.gbff_pp10.ffn GCA_900124925.1_17138_2_42_genomic.gbff_pp13.ffn GCA_900126435.1_17175_2_81_genomic.gbff_pp6.ffn GCA_900251565.1_M3684_genomic.gbff_pp14.ffn GCA_900251895.1_M3925_genomic.gbff_pp17.ffn GCA_900251895.1_M3925_genomic.gbff_pp18.ffn GCA_900323905.1_24117-WT_genomic.gbff_pp6.ffn GCA_900482705.1_7915_6_43_genomic.gbff_pp16.ffn
GCA_000009005.1_ASM900v1_genomic.gbff_pp10.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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GCA_003420165.1_ASM342016v1_genomic.gbff_pp15.ffn 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_003422165.1_ASM342216v1_genomic.gbff_pp20.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 3 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 5 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900035925.1_7712_8_30_genomic.gbff_pp17.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900040545.1_7469_7_44_genomic.gbff_pp11.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900080895.1_12625_5_54_genomic.gbff_pp2.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900081425.1_12641_2_75_genomic.gbff_pp1.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900081425.1_12641_2_75_genomic.gbff_pp21.ffn 0 0 0 0 0 0 0 0 0 0 0 0 73 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900081425.1_12641_2_75_genomic.gbff_pp22.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 12 0 5 0 0 5 10 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900081945.1_12673_2_15_genomic.gbff_pp10.ffn 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900124925.1_17138_2_42_genomic.gbff_pp13.ffn 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900126435.1_17175_2_81_genomic.gbff_pp6.ffn 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900251565.1_M3684_genomic.gbff_pp14.ffn 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900251895.1_M3925_genomic.gbff_pp17.ffn 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 9 7 0 5 0 0 10 4 0 0 0 7 0 0 0 0 0 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 5 0 0 0 0 6 0 20 0 9 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
GCA_900251895.1_M3925_genomic.gbff_pp18.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900323905.1_24117-WT_genomic.gbff_pp6.ffn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GCA_900482705.1_7915_6_43_genomic.gbff_pp16.ffn 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3 0 2 0 0 4 3 0 0 0 4 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 2 0 4 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0

Optional

Example of getting lower and upper triangular of a matrix

In [17]:
A = np.array([[2,3,4], [3,45,8], [34,7,0.8], [21,31,41]])

print(f'A : \n {A}')
print(f'A^T is : \n {A.T}')

S = np.matmul(A,A.T)

print(f'Symmetric Matrix 1: \n {S}')

A : 
 [[ 2.   3.   4. ]
 [ 3.  45.   8. ]
 [34.   7.   0.8]
 [21.  31.  41. ]]
A^T is : 
 [[ 2.   3.  34.  21. ]
 [ 3.  45.   7.  31. ]
 [ 4.   8.   0.8 41. ]]
Symmetric Matrix 1: 
 [[  29.    173.     92.2   299.  ]
 [ 173.   2098.    423.4  1786.  ]
 [  92.2   423.4  1205.64  963.8 ]
 [ 299.   1786.    963.8  3083.  ]]

Now extract upper and lower tringulars:

In [18]:
upper = np.triu(S)
upper
Out[18]:
array([[  29.  ,  173.  ,   92.2 ,  299.  ],
       [   0.  , 2098.  ,  423.4 , 1786.  ],
       [   0.  ,    0.  , 1205.64,  963.8 ],
       [   0.  ,    0.  ,    0.  , 3083.  ]])
In [19]:
lower = np.tril(S)
lower
Out[19]:
array([[  29.  ,    0.  ,    0.  ,    0.  ],
       [ 173.  , 2098.  ,    0.  ,    0.  ],
       [  92.2 ,  423.4 , 1205.64,    0.  ],
       [ 299.  , 1786.  ,  963.8 , 3083.  ]])